Module « pandas »
Signature de la fonction bdate_range
def bdate_range(start=None, end=None, periods=None, freq='B', tz=None, normalize=True, name=None, weekmask=None, holidays=None, closed=None, **kwargs) -> pandas.core.indexes.datetimes.DatetimeIndex
Description
bdate_range.__doc__
Return a fixed frequency DatetimeIndex, with business day as the default
frequency.
Parameters
----------
start : str or datetime-like, default None
Left bound for generating dates.
end : str or datetime-like, default None
Right bound for generating dates.
periods : int, default None
Number of periods to generate.
freq : str or DateOffset, default 'B' (business daily)
Frequency strings can have multiples, e.g. '5H'.
tz : str or None
Time zone name for returning localized DatetimeIndex, for example
Asia/Beijing.
normalize : bool, default False
Normalize start/end dates to midnight before generating date range.
name : str, default None
Name of the resulting DatetimeIndex.
weekmask : str or None, default None
Weekmask of valid business days, passed to ``numpy.busdaycalendar``,
only used when custom frequency strings are passed. The default
value None is equivalent to 'Mon Tue Wed Thu Fri'.
holidays : list-like or None, default None
Dates to exclude from the set of valid business days, passed to
``numpy.busdaycalendar``, only used when custom frequency strings
are passed.
closed : str, default None
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None).
**kwargs
For compatibility. Has no effect on the result.
Returns
-------
DatetimeIndex
Notes
-----
Of the four parameters: ``start``, ``end``, ``periods``, and ``freq``,
exactly three must be specified. Specifying ``freq`` is a requirement
for ``bdate_range``. Use ``date_range`` if specifying ``freq`` is not
desired.
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Note how the two weekend days are skipped in the result.
>>> pd.bdate_range(start='1/1/2018', end='1/08/2018')
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-08'],
dtype='datetime64[ns]', freq='B')
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